Paroxysmal Atrial Fibrillation Prediction Application Using Genetic Algorithms

Mota, Sonia; Ros, Eduardo; Toro, Francisco de; Ortega, Julio

doi:10.1007/3-540-44864-0_105

Cited by 1 publication

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References 11 publications

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“…A correlation between the rate and shape of atrial premature complexes and AF onset was mainly found (Thong et al 2004). Finally, other tentative predictors can be mentioned, such as those based on the heart rate variability (HRV) analysis (Shin et al 2006) or genetic algorithms (Mota et al 2003); however, none of them have been capable of providing useful AF predictors.…”

Section: Introductionmentioning

confidence: 99%

Wavelet bidomain sample entropy analysis to predict spontaneous termination of atrial fibrillation

Alcaraz

Rieta²

2008

Physiol. Meas.

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The ability to predict if an atrial fibrillation (AF) episode terminates spontaneously or not through non-invasive techniques is a challenging problem of great clinical interest. This fact could avoid useless therapeutic interventions and minimize the risks for the patient. The present work introduces a robust AF prediction methodology carried out by estimating, through sample entropy (SampEn), the atrial activity (AA) organization increase prior to AF termination from the surface electrocardiogram (ECG). This regularity variation appears as a consequence of the decrease in the number of reentries wandering throughout the atrial tissue. AA was obtained from surface ECG recordings by applying a QRST cancellation technique. Next, a robust and reliable classification process for terminating and non-terminating AF episodes was developed, making use of two different wavelet decomposition strategies. Finally, the AA organization both in time and wavelet domains (bidomain) was estimated via SampEn. The methodology was validated using a training set consisting of 20 AF recordings with known termination properties and a test set of 30 recordings. All the training signals and 93.33% of the test set were correctly classified into terminating and sustained AF, obtaining 93.75% sensitivity and 92.86% specificity. It can be concluded that spontaneous AF termination can be reliably and noninvasively predicted by applying wavelet bidomain sample entropy.

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Section: Introductionmentioning

confidence: 99%